AI Financial Tools: Risks and Benefits of AI in Finance

Today, we are witnessing the continuous and rapid development of artificial intelligence (AI) and its implementation in various fields. Modern software solutions in the financial sector already include blockchain technology and digital currencies, but can they be expanded to incorporate more contemporary developments?

In this blog post, the experts at Grapherex discuss the role of AI in increasing the efficiency of financial management and the potential risks associated with its implementation.

AI Tools in the Financial Sector

The concept of AI has been around for a long time, and it is now being actively implemented in financial services. AI influences areas such as robo-advising, algorithmic trading, fraud and compliance, credit scoring, and predicting financial distress. AI can execute processes faster than humans and make inferences that humans might miss.

There are several examples of AI tools that could be used in financial institutions. Let’s talk about the use cases of artificial intelligence in more detail.

ChatGPT and Bing Chat

AI tools such as ChatGPT and Bing Chat have demonstrated an impressive ability to improve efficiency because they can process huge amounts of data quickly. Bots are so good that 7,800 jobs at IBM risk being replaced by AI within a few years, according to the company’s CEO.

ChatGPT is a language model created by OpenAI. It generates conversational responses in a chat-based format. ChatGPT is used in various apps for answering questions and providing explanations, just like a support centre worker. Although the software is advanced, it may not always produce accurate responses. It also has a knowledge cut-off of September 2021.

Bing AI is a new feature of Microsoft’s Bing search engine that uses AI to provide better search results, more complete answers, and the ability to generate content. Bing Chat uses GPT4 and is free. It is also connected to the Internet, which means that it can receive information beyond 2021. Bing provides confirmation links to almost every generated thesis.

Machine Learning

Machine learning has huge potential in finance. ML is a subset of AI that learns from data and has the ability to mimic human decisions. The more the system learns, the better answers and the more accurate predictions it gives. In a 2022 report, trading platform Robinhood noted that its machine-learning models were already highly advanced and were adding value to many business opportunities.

AI Assistants in the Crypto Industry

AI assistants within the crypto space provide automated support and give users information on the topics they are interested in. They can tell users about various cryptocurrencies, blockchain technology, trading methods, and more.

In March 2023, cryptocurrency exchange Crypto.com released a generative AI user assistant called Amy. Binance launched an AI-powered NFT token generator, which minted over 10,000 tokens in less than three hours.

Important Success Factors

Below we talk about the limitations of modern developments. But let’s start on the positive side. To reduce the number of potential errors in the operation of AI, we must be careful with the material on which it is trained. Several aspects affect AI performance and efficiency.

Here are some of the factors highlighted in the Payments Association’s guide to AI:

• Clean and aggregated data
• Effective AI supervision
• AI accountability
• Enabling regulation

The most important factor is that clean, reliable, and comprehensive data is used during training; this helps to ensure that AI makes accurate predictions. Data should be taken from various reliable sources and structured properly. Once the data is deemed sufficient, it needs to be aggregated so that the AI can analyse and draw conclusions from multiple internal and external datasets.

Secondly, ongoing monitoring and testing are necessary to improve decision-making, eliminate bias, and ensure confidentiality and security. Verification is also crucial for regulatory compliance. Thirdly, the distinction between “black box” and “white box” is important to ensure accountability. White-box AI algorithms that promote explainability and accountability are preferable to black-box algorithms.

Finally, for AI to function properly, we need appropriate laws to enable it to be embedded in the financial sector. The European Union has proposed an Artificial Intelligence Act to regulate AI and address issues of bias, discrimination, privacy, and human rights violations. The proposed rules would particularly impact banking and finance. However, implementing these regulations could prove costly and, according to estimates by the Center for Data Innovation, could exceed $30 million.

AI Limitations and Risks

Now that we have discussed everything related to the success factors, let’s move on to the future and the potential barriers and constraints that AI faces.

AI tools cannot fully support individuals during financially challenging times. AI-based providers may lack the capacity for empathy, understanding, and decision-making that human consultants or support systems can provide. Algorithmic bias is another legitimate concern, as AI may unintentionally favour or disadvantage potential ideas based on the biases emanating from its model.

There is a need to monitor, supervise, and adjust AI in finance in line with market dynamics. It includes modelling pitfalls and considering measures for cybersecurity and data privacy. This should be done to improve system stability and reduce vulnerability to failure.

The risks of mass job displacement seem overestimated but, at the same time, relevant. The assertation above from the CEO of IBM confirms this. However, just as computers and smartphones have not replaced humans but prompted them to explore the topic and join in, so it will be with artificial intelligence. There are likely to be (and are already emerging) a huge number of professions related to the training and maintenance of AI systems.

Will AI Help with Finance Management?

Artificial intelligence has transformative potential in the financial sector. Here’s a short list of how algorithms can help accountants in their work:

• Assessment and management of risk
• Fraud detection and prevention
• Effective credit and trading decisions
• 24/7 customer support services
• Automation of recurrent processes
• Reduction of human error

Robert Quartly-Janeiro, a chief strategist at crypto exchange Bitrue, said that modern AI tools are part of the future, going far beyond their current applications. Businesses will use these tools if they save money, he added. In his opinion, AI can help change the retail finance sector if it manages to provide fairer credit solutions, a more open approach to consumer lending, better risk management, and increased access to finance.

Chris Ainsworth is CEO of Pave Finance, a service that uses AI to monitor market conditions and personalise portfolios. He said that AI financial tools are not currently ready for use without oversight. Current AI tools can lag, he stated, so it will take much longer than people think. Ainsworth added that supervision is necessary to ensure models are set up correctly to account for volatility and market changes.

Overall, AI tools are likely to be a paradigm shift in trading, banking, and financial advisory. Additionally, future AI tools will help reduce the costs of managing and maintaining products in the financial sector.